VPN adoption has seen steady growth over the past decade due to increased public awareness of privacy and surveillance threats. In response, certain governments are attempting to restrict VPN access by identifying connections using “dual use” DPI ...

Privacy notice and choice has largely failed us so far because we are not giving it the legal and technical support it needs.

Trade-offs between secure computation via cryptography and hardware enclaves.

Federated learning (FL) has great potential for large-scale machine learning (ML) without exposing raw data. Differential privacy (DP) is the de facto standard of privacy protection with provable guarantees. Advances in ML suggest that DP would be a ...

Building federated learning with differential privacy to train and refine machine-learning models with more comprehensive datasets can help exploit the potential of machine learning to its fullest.

Side-channel attacks, such as Spectre, rely on properties of modern CPUs that permit discovery of microarchitectural state via timing of various operations. The Weird Machine concept is an increasingly popular model for characterization of execution that ...$$$$$$$$

From lessons learned to new directions.

Focusing on "enabling approaches" that treat humans as partners can add another layer of protection to our cybersecurity defenses.

With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas. This development has influenced computer security, spawning a ...

Implementing confidential computing on Azure container instances.

Attestation and verification are integral to adopting confidential computing.

To counter the threat of AI-powered bots, captcha systems are growing increasingly complex, often sacrificing usability and accessibility. Can mCaptcha, a proof-of-work captcha system, effectively address these issues?

Federated learning and confidential computing are not competing technologies.

Multidisciplinary experts’ perspectives on how to strengthen protection of patients’ health information in telehealth designs and workflows.